{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import sys,random\n",
    "import pickle\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modin.pandas as pd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('featurescore'):\n",
    "    os.mkdir('featurescore')\n",
    "if not os.path.exists('model'):\n",
    "    os.mkdir('model')\n",
    "if not os.path.exists('preds'):\n",
    "    os.mkdir('preds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 29s, sys: 11.4 s, total: 2min 40s\n",
      "Wall time: 2min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_date = pd.read_csv('../../preprocess_data_new/train_ax_date.csv').drop(columns=['id'])\n",
    "data_row_null = pd.read_csv('../../preprocess_data_new/train_ax_row_null.csv').drop(columns=['id'])\n",
    "data_cat = pd.read_csv('../../preprocess_data_new/train_ax_cat.csv').drop(columns=['id']) \n",
    "data_cont = pd.read_csv('../../preprocess_data_new/train_ax_cont.csv') # 忘记导入id这一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.7 s, sys: 236 ms, total: 17 s\n",
      "Wall time: 17 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data = pd.concat([data_date,data_row_null,data_cat,data_cont],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.7 s, sys: 1.01 s, total: 26.7 s\n",
      "Wall time: 41.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_tag = pd.read_csv('../../preprocess_data_new/train_ax1.csv',usecols=['tag'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 本地验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dtrain' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-6977da0f1f5b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m         }\n\u001b[1;32m     22\u001b[0m \u001b[0;31m#3.训练模型\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mwatchlist\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0mdtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'开始训练！'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dtrain' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split # 分割数据模块\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn import metrics\n",
    "#1.分割数据\n",
    "X_train, X_test, y_train, y_test = train_test_split(data.values,data_tag.values,test_size=0.3, random_state=0)\n",
    "#2.建立模型\n",
    "params={\n",
    "    'booster':'gbtree',\n",
    "    'objective': 'binary:logistic',\n",
    "    'early_stopping_rounds':100,\n",
    "    'scale_pos_weight': float(len(data_tag)-np.sum(data_tag.values))/float(np.sum(data_tag.values)),  # 负例样本除以正例样本\n",
    "    'eval_metric': 'auc',\n",
    "    'gamma':1,\n",
    "    'max_depth':6,\n",
    "    'lambda':1,\n",
    "    'subsample':0.9,\n",
    "    'colsample_bytree':0.9,\n",
    "    'min_child_weight':1, \n",
    "    'eta': 0.04,\n",
    "    'seed':1080,\n",
    "    'nthread':16\n",
    "        }\n",
    "#3.训练模型\n",
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "watchlist  = [(dtrain,'train')]\n",
    "print('开始训练！')\n",
    "model  = xgb.train(params,dtrain,num_boost_round=800,evals=watchlist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始预测！\n",
      "AUC: 0.934890475530805\n"
     ]
    }
   ],
   "source": [
    "# 4.预测结果\n",
    "print('开始预测！')\n",
    "dtest = xgb.DMatrix(X_test)\n",
    "y_pre = model.predict(dtest)\n",
    "auc = metrics.roc_auc_score(y_test, y_pre)\n",
    "print('AUC:',auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练与预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 29.3 s, sys: 4.82 s, total: 34.1 s\n",
      "Wall time: 36.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "valid_date = pd.read_csv('../../preprocess_data_new/valid_date.csv').drop(columns=['id'])\n",
    "valid_row_null = pd.read_csv('../../preprocess_data_new/valid_row_null.csv').drop(columns=['id'])\n",
    "valid_cat = pd.read_csv('../../preprocess_data_new/valid_cat.csv').drop(columns=['id']) \n",
    "valid_cont = pd.read_csv('../../preprocess_data_new/valid_cont.csv') # 忘记导入id这一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.2 s, sys: 76 ms, total: 2.28 s\n",
      "Wall time: 2.28 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "valid = pd.concat([valid_date,valid_row_null,valid_cat,valid_cont],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(data.values, data_tag.values, feature_names=list(data.columns))\n",
    "dtest = xgb.DMatrix(valid.values, feature_names=list(valid.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_id = pd.read_csv('../../preprocess_data_new/valid_date.csv',usecols=['id']).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(dtrain,dtest,test_id,iteration,random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight):\n",
    "    if max_depth==6:\n",
    "        num_boost_round = 800\n",
    "    elif max_depth==7:\n",
    "        num_boost_round = 650\n",
    "    elif max_depth==8:\n",
    "        num_boost_round = 500\n",
    "    \n",
    "    params={\n",
    "    'booster':'gbtree',\n",
    "    'objective': 'binary:logistic',\n",
    "    'early_stopping_rounds':100,\n",
    "    'scale_pos_weight': float(len(data_tag)-np.sum(data_tag.values))/float(np.sum(data_tag.values)),  # 负例样本除以正例样本\n",
    "    'eval_metric': 'auc',\n",
    "    'gamma':gamma,\n",
    "    'max_depth':max_depth,\n",
    "    'lambda':lambd,\n",
    "    'subsample':subsample,\n",
    "    'colsample_bytree':colsample_bytree,\n",
    "    'min_child_weight':min_child_weight, \n",
    "    'eta': 0.04,\n",
    "    'seed':random_seed,\n",
    "    'nthread':16\n",
    "        }\n",
    "    watchlist  = [(dtrain,'train')]\n",
    "    model = xgb.train(params,dtrain,num_boost_round=num_boost_round,evals=watchlist)\n",
    "    model.save_model('./model/xgb{0}.model'.format(iteration))\n",
    "    \n",
    "    #predict test set\n",
    "    test_y = model.predict(dtest)\n",
    "    test_result = pd.DataFrame(test_id,columns=[\"id\"])\n",
    "    test_result['score'] = test_y\n",
    "    test_result.to_csv(\"./preds/xgb{0}.csv\".format(iteration),index=None,encoding='utf-8')\n",
    "    \n",
    "    #get feature score\n",
    "    feature_score = model.get_fscore()\n",
    "    feature_score = sorted(feature_score.items(), key=lambda x:x[1],reverse=True)\n",
    "    fs = []\n",
    "    for (key,value) in feature_score:\n",
    "        fs.append(\"{0},{1}\\n\".format(key,value))\n",
    "    \n",
    "    with open('./featurescore/feature_score_{0}.csv'.format(iteration),'w') as f:\n",
    "        f.writelines(\"feature,score\\n\")\n",
    "        f.writelines(fs)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.830037\n",
      "[1]\ttrain-auc:0.842366\n",
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      "[649]\ttrain-auc:0.954503\n",
      "CPU times: user 23h 9min 6s, sys: 20min 5s, total: 23h 29min 11s\n",
      "Wall time: 1h 36min 24s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "random_seed = list(range(1000,2000,20))\n",
    "gamma = [i/1000.0 for i in range(100,200,2)]\n",
    "max_depth = [6,7,8]\n",
    "lambd = list(range(200,400,2))\n",
    "subsample = [i/1000.0 for i in range(600,700,2)]\n",
    "colsample_bytree = [i/1000.0 for i in range(250,350,2)]\n",
    "min_child_weight = [i/1000.0 for i in range(200,300,2)]\n",
    "random.shuffle(random_seed)\n",
    "random.shuffle(gamma)\n",
    "random.shuffle(max_depth)\n",
    "random.shuffle(lambd)\n",
    "random.shuffle(subsample)\n",
    "random.shuffle(colsample_bytree)\n",
    "random.shuffle(min_child_weight)\n",
    "\n",
    "with open('params.pkl','wb') as f:\n",
    "    pickle.dump((random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight),f)\n",
    "\n",
    "#to reproduce my result, uncomment following lines\n",
    "\"\"\"\n",
    "with open('params_for_reproducing.pkl','rb') as f:\n",
    "    random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight = pickle.load(f)    \n",
    "\"\"\"\n",
    "\n",
    "for i in range(8):\n",
    "    pipeline(dtrain,dtest,test_id,i,random_seed[i],gamma[i],max_depth[i%3],lambd[i],subsample[i],colsample_bytree[i],min_child_weight[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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